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Towards Generating Policy-compliant Datasets (poster)

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"Towards Generating Policy-compliant Datasets" by Christophe Debruyne, Harshvardhan J. Pandit, Dave Lewis, Declan O’Sullivan. Presented at the The 13th IEEE International Conference on SEMANTIC COMPUTING
Jan 30 - Feb 1, 2019, Newport Beach, California

Publié dans : Sciences
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Towards Generating Policy-compliant Datasets (poster)

  1. 1. • Data Structure Definitions • Dimensions • Measures • Attributes • References to tables • References to columns • Purpose, policies • ... Mapping Engine R2RML Mapping R2RML Processor extended with Data Cube Dataset (G1) according to ValidationProvenance Information captured with Organization’s databases Consent Information Filter Data Cube Dataset Compliant Data Cube Dataset (G1’) Towards Generating Policy-compliant Datasets Context and Problem • Datasets are created and used for a specific purpose, but such data processing is increasingly the subject of various internal and external regulations – e.g., GDPR. • One particular aspect of GDPR is informed consent, which must by given for these purposes. • SOTA focuses on compliance analysis of processes; either by analyzing the processes before execution or post-hoc analysis of logs. • Our hypothesis is that compliance verification can be facilitated by generating datasets “on demand”. Research Question • Can we generate datasets for a specific purpose “just in time” that complies with informed consent? Goal • To propose a method for generating datasets that are fit for a specific purpose and taking into account the ever evolving informed consent of people in a declarative manner, availing of semantic technologies. Potential Impact • Facilitating compliance verification as part of data governance best practices within an organization The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. References and Links 1. Christophe Debruyne, Dave Lewis, Declan O'Sullivan: Generating Executable Mappings from RDF Data Cube Data Structure Definitions. OTM Conferences (2) 2018: 333-350 • Ontology: https://w3id.org/consent-mapping-jit • Experiment: https://scss.tcd.ie/~debruync/icsc2019/ Demonstration and Results • We demonstrated the viability of our approach, using a synthetic dataset, though more experiments are called for. • All intermediate graphs allow one to trace the various steps – traceability and transparency (provenance) Future Work • A current limitation is a lack of evaluation beyond the synthetic dataset created for the study. • We furthermore recognize the opportunities in aligning or integrating our models and approach with related work. Christophe Debruyne w Harshvardhan J. Pandit w Dave Lewis w Declan O’Sullivan ADAPT, Trinity College Dublin, Dublin 2, Ireland {first.last}@adaptcentre.ie Approach Building upon R2DQB [1], allowing one to annotate RDF Data Cube data structure definitions to generate R2RML mappings that will create a RDF Data Cube dataset. See http://openscience.adaptcentre.ie/ for more of our projects.